A technique for real-time detecting, locating, and quantifying damage in large polymer composite structures made of carbon fibers and carbon nanotube networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A significant safety concern preventing extensive use of composite materials for large polymer composite structures is the ability to detect, locate, and quantify damages that occur at one or several locations in large polymer composite structures. Real-time health monitoring of large polymer composite structures improves their performance, durability, and reliability while minimizing the life cycle cost. In this article, we present a new, practical, and real-time structural health monitoring technique for detecting, locating, and quantifying damages in large polymer composite structures made of carbon fibers and carbon nanotube networks. In this technique, electrically conductive epoxy resin was prepared by dispersing multiwalled carbon nanotubes into epoxy matrix. This modified epoxy matrix was then incorporated with long carbon fibers to make large composite plates. Two sets of grid points made from silver-epoxy paste were mounted on the surface of the large plates. The first set was used to apply the constant electric current, and the second set was utilized to measure the electric potential. The electric potentials across the second set of grid points on the undamaged plate were measured and used as a reference set. Two different damages were created by drilling holes and by applying impact loading on the large plates. It is found that the electric potential between the contact points surrounding the damage changes. The significant change in electric potential corresponds to the damage location in the plates. As such, drilled holes, impact damages, and barely visible impact damages are detected, located, and quantified.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it